Understanding the core function of the brain is one the major challenges of our times. In the areas of
neuroscience and education, several new studies try to correlate the learning difficulties faced by children
and youth with behavioral and social problems. This work aims to present the challenges and opportunities
of computational neuroscience research, with the aim of detecting people with learning disorders. We
present a line of investigation based on the key areas: neuroscience, cognitive sciences and computer
science, which considers young people between nine and eighteen years of age, with or without a learning
disorder. The adoption of neural networks reveals consistency in dealing with pattern recognition problems
and they are shown to be effective for early detection in patients with these disorders. We argue that
computational neuroscience can be used for identifying and analyzing young Brazilian people with several
cognitive disorders.

Grid computing has consolidated itself as a solution able of integrating, on a global scale, heterogeneous resources distributed geographically. This fact has contributed significantly to increase the IT infrastructure. However, all this computer power results in a lot of energy consumption, raising concerns not only with respect to economic aspects, but also regarding environmental impacts. Current data shows that the information technology and communication industry has been responsible for 2% of the carbon dioxide global emission, equivalent to the entire aviation industry. This paper proposes a biobjective strategy for resource allocation on global scientific grids, considering both energy consumption and execution times. An algorithm is presented which generates the minimal complete set of Pareto-optimal solutions in polynomial time. Computation experience is reported for three distinct scenarios.

Business Provenance provides important documentation that is an essential to increase the trustworthiness and traceability of end-to-end business operations. This paper presents two data marts that allows multidimensional analysis of business provenance metadata collected from a real e-business scenario. Provenance was collected with the aid of an architecture named BizProv. We conclude the paper with the identification of the challenges that will drive future research of BizProv.